Semantic segmentation, which assigns semantic labels to each pixel in an image, is an essential tasks for many computer vision applications.
Deep neural networks show remarkable success in semantic segmentation (e.g. [1]}, [2]}). However, the success of current deep semantic segmentation methods depend on having large quantities of manually annotated data which may be difficult to produce for dense prediction tasks. As shown in [3]}, the performance of a semantic segmentation network degrades rapidly with limited annotations even with carefully tuned networks. Further, data-driven approaches with limited data may not generalize well outside the training environment.
